Guest Editors
Prof. Muhammad Shahid Anwar
Email: shahidanwar786@gachon.ac.kr
Affiliation: Department of AI and Software, Gachon University, 13120, South Korea
Homepage:
Research Interests: HCI, Immersive technology, QoE, Metaverse
Prof. Ikram Syed
Email: ikram@hufs.ac.kr
Affiliation: Dept Information & Communication Engineering, Hankuk University of Foreign Studies, Yongin, 17035, South Korea.
Homepage:
Research Interests: Machine Learning, HCI, Internet of Things
Summary
Action recognition is a critical area of research within computer vision and artificial intelligence, focused on the automatic identification and interpretation of human actions in videos or images. This technology has vast applications, from surveillance and security to healthcare, human-computer interaction, sports analytics, autonomous vehicles, and entertainment. Recent advances in deep learning, sensor fusion, and multimodal analysis have significantly enhanced the accuracy and efficiency of action recognition systems, opening new possibilities and challenges in both academic research and industry applications.
The special issue aims to bring together cutting-edge research contributions that address the latest developments, challenges, and future directions in the field of action recognition. This special issue will serve as a comprehensive platform for researchers and practitioners to share innovative methods, present novel applications, and discuss the technical challenges and potential solutions in the rapidly evolving landscape of action recognition.
Topics of Interest:
We invite high-quality submissions on, but not limited to, the following topics:
- Deep learning architectures (CNNs, RNNs, GNNs, Transformers) and learning techniques for action recognition.
- Multimodal action recognition using data fusion from RGB, depth, skeletal data, audio, etc.
- Real-time and efficient action recognition models for edge devices and resource-constrained environments.
- 3D and skeleton-based action recognition, including techniques leveraging human pose estimation and motion dynamics.
- Weakly supervised, zero-shot, and few-shot learning approaches for recognizing actions with limited or no labeled data.
- Action recognition in Extended Reality (XR) environments: Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) applications.
- Applications in healthcare, sports analytics, entertainment, security, autonomous systems, etc.
- Development of new datasets, benchmarks, and evaluation metrics for action recognition.
- Explainability and interpretability of action recognition models, including visualization techniques and ethical considerations.
Keywords
Action Recognition; Deep Learning; Multimodal Analysis; 3D Vision; Extended Reality (XR); Real-Time Processing; Zero-Shot Learning; Human Activity Recognition; Sensor Fusion; Explainable AI
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